Improving Marketing ROI for an E-Commerce Brand Using BI-Powered Ad Spend Analytics

A fast-growing direct-to-consumer (D2C) e-commerce company operating in the fashion and accessories segment struggled to understand the true return on investment (ROI) from its multi-channel marketing efforts. With significant monthly ad spend across Meta, Google Ads, and influencer campaigns, the brand lacked unified visibility into which channels were driving profitable customer conversions and long-term value. To address this, a robust Business Intelligence (BI) solution was implemented to centralize campaign, conversion, and customer lifetime value (LTV) data. By integrating marketing analytics, purchase history, and CRM data, the company was able to track return on ad spend (ROAS) in real time, segment customer cohorts, and optimize budget allocation toward the highest-performing sources

Problem Statement

Detached information sources made it troublesome to trait deals to particular campaigns or stages
The team lacked a unified dashboard to compare spend, conversion rates, and customer LTV
Marketing budgets were often allocated reactively rather than based on performance
High ad spends were generating traffic but not always driving profitable conversions
Influencer and email marketing efforts lacked measurable impact tracking

Solution Approach

Solidified advertisement execution information from Meta, Google Advertisements, and influencer spreadsheets into a centralized information stockroom (BigQuery)
Integrated Shopify sales and CRM data to attribute conversions to specific campaigns
Developed ROAS dashboards segmented by platform, campaign, ad creative, and landing page
Built LTV prediction models in Python to evaluate long-term impact of each acquisition source
Identified underperforming campaigns and reallocated budget to high-ROAS ad sets
Enabled the marketing team with real-time dashboards to guide daily budget adjustments
Deployed weekly automated reports for C-level decision-makers and campaign owners

Benefits

35% increment in mixed ROAS over all paid channels
28% drop in customer acquisition costs over 6 months
Improved marketing efficiency by identifying low-performing ad creatives and campaigns
Higher revenue per user by focusing on channels with better LTV
Real-time bits of knowledge enabled showcasing groups to form speedier, data-driven choices

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